Earth Observation

133 papers with code • 0 benchmarks • 0 datasets

Earth Observation (EO) refers to the use of remote sensing technologies to monitor land, marine (seas, rivers, lakes) and atmosphere. Satellite-based EO relies on the use of satellite-mounted payloads to gather imaging data about the Earth’s characteristics. The images are then processed and analyzed in order to extract different types of information that can serve a very wide range of applications and industries.


Use these libraries to find Earth Observation models and implementations

Most implemented papers

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

phelber/EuroSAT 31 Aug 2017

We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27, 000 labeled and geo-referenced images.

DOTA: A Large-scale Dataset for Object Detection in Aerial Images

PaddlePaddle/PaddleDetection CVPR 2018

The fully annotated DOTA images contains $188, 282$ instances, each of which is labeled by an arbitrary (8 d. o. f.)

Proximity Forest: An effective and scalable distance-based classifier for time series

fpetitjean/ProximityForest 31 Aug 2018

We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100, 000 times faster than current state of the art models Elastic Ensemble and COTE.

Fully Convolutional Siamese Networks for Change Detection

likyoo/open-cd 19 Oct 2018

This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images.

End-to-end learning of energy-based representations for irregularly-sampled signals and images

CIA-Oceanix/DinAE 1 Oct 2019

In this paper, we address the end-to-end learning of representations of signals, images and image sequences from irregularly-sampled data, i. e. when the training data involved missing data.

Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks

PatrickTUM/SEN12MS-CR-TS 14 Dec 2019

In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.

SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation

zhu-xlab/ssl4eo-s12 13 Nov 2022

Self-supervised pre-training bears potential to generate expressive representations without human annotation.

Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework

lizhuohong/segland 19 Apr 2024

Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel's land-cover type of remote-sensing images.

Self-attention for raw optical Satellite Time Series Classification

marccoru/crop-type-mapping 23 Oct 2019

The amount of available Earth observation data has increased dramatically in the recent years.

Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network

I-Hope-Peace/ChangeDetectionRepository 18 Dec 2019

Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection.